Incluye bibliografía: páginas 731-750 e índice: páginas 751-767

Preface to the Second Edition.. Preface to the First Edition.. Chapter 1.. Introduction.. 1.1 What Is Computational Statistics?.. 1.2 An Overview of the Book.. Philosophy.. What Is Covered.. A Word About Notation.. 1.3 MATLAB® Code.. Computational Statistics Toolbox.. Internet Resources.. 1.4 Further Reading.. Chapter 2.. Probability Concepts.. 2.1 Introduction.. 2.2 Probability.. Background.. Probability.. Axioms of Probability.. 2.3 Conditional Probability and Independence.. Conditional Probability.. Independence.. Bayes' Theorem.. 2.4 Expectation.. Mean and Variance.. Skewness.. Kurtosis.. 2.5 Common Distributions.. Binomial.. Poisson.. Uniform.. Normal.. Exponential.. Gamma.. Chi-Square.. Weibull.. Beta.. Student's t Distribution.. Multivariate Normal.. Multivariate t Distribution.. 2.6 MATLAB® Code.. 2.7 Further Reading.. .. Exercises.. Chapter 3.. Sampling Concepts.. 3.1 Introduction.. 3.2 Sampling Terminology and Concepts.. Sample Mean and Sample Variance.. Sample Moments.. Covariance.. 3.3 Sampling Distributions.. 3.4 Parameter Estimation.. Bias.. Mean Squared Error.. Relative Efficiency.. Standard Error.. Maximum Likelihood Estimation.. Method of Moments.. 3.5 Empirical Distribution Function.. Quantiles.. 3.6 MATLAB® Code.. 3.7 Further Reading.. Exercises.. Chapter 4.. Generating Random Variables.. 4.1 Introduction.. 4.2 General Techniques for Generating Random Variables.. Uniform Random Numbers.. Inverse Transform Method.. Acceptance-Rejection Method.. 4.3 Generating Continuous Random Variables.. Normal Distribution.. Exponential Distribution.. Gamma.. Chi-Square.. Beta.. Multivariate Normal.. Multivariate Student's t Distribution.. Generating Variates on a Sphere.. 4.4 Generating Discrete Random Variables.. Binomial.. Poisson.. Discrete Uniform.. 4.5 MATLAB® Code.. 4.6 Further Reading.. Exercises.. Chapter 5.. Exploratory Data Analysis.. 5.1 Introduction.. 5.2 Exploring Univariate Data

Histograms.. Stem-and-Leaf.. Quantile-Based Plots - Continuous Distributions.. Quantile Plots - Discrete Distributions.. Box Plots.. 5.3 Exploring Bivariate and Trivariate Data.. Scatterplots.. Surface Plots.. Contour Plots.. Bivariate Histogram.. 3-D Scatterplot.. 5.4 Exploring Multi-Dimensional Data.. Scatterplot Matrix.. Slices and Isosurfaces.. Glyphs.. Andrews Curves.. Parallel Coordinates.. 5.5 MATLAB® Code.. 5.6 Further Reading.. Exercises.. Chapter 6.. Finding Structure.. 6.1 Introduction.. 6.2 Projecting Data.. 6.3 Principal Component Analysis.. 6.4 Projection Pursuit EDA.. Projection Pursuit Index.. Finding the Structure.. Structure Removal.. 6.5 Independent Component Analysis.. 6.6 Grand Tour.. 6.7 Nonlinear Dimensionality Reduction.. Multidimensional Scaling.. Isometric Feature Mapping - ISOMAP.. 6.8 MATLAB® Code.. 6.9 Further Reading.. Exercises.. Chapter 7.. Monte Carlo Methods for Inferential Statistics.. 7.1 Introduction.. 7.2 Classical Inferential Statistics.. Hypothesis Testing.. Confidence Intervals.. 7.3 Monte Carlo Methods for Inferential Statistics.. Basic Monte Carlo Procedure.. Monte Carlo Hypothesis Testing.. Monte Carlo Assessment of Hypothesis Testing.. 7.4 Bootstrap Methods.. General Bootstrap Methodology.. Bootstrap Estimate of Standard Error.. Bootstrap Estimate of Bias.. Bootstrap Confidence Intervals.. 7.5 MATLAB® Code.. 7.6 Further Reading.. Exercises.. Chapter 8.. Data Partitioning.. 8.1 Introduction.. 8.2 Cross-Validation.. 8.3 Jackknife.. 8.4 Better Bootstrap Confidence Intervals.. 8.5 Jackknife-After-Bootstrap.. 8.6 MATLAB® Code.. 8.7 Further Reading.. Exercises.. Chapter 9.. Probability Density Estimation.. 9.1 Introduction.. 9.2 Histograms.. 1-D Histograms.. Multivariate Histograms.. Frequency Polygons.. Averaged Shifted Histograms.. 9.3 Kernel Density Estimation.. Univariate Kernel Estimators.. Multivariate Kernel Estimators.. 9.4 Finite Mixtures .. Univariate Finite Mixtures

Visualizing Finite Mixtures.. Multivariate Finite Mixtures.. EM Algorithm for Estimating the Parameters.. Adaptive Mixtures.. 9.5 Generating Random Variables.. 9.6 MATLAB® Code.. 9.7 Further Reading.. Exercises.. Chapter 10.. Supervised Learning.. 10.1 Introduction.. 10.2 Bayes Decision Theory.. Estimating Class-Conditional Probabilities: Parametric Method.. Estimating Class-Conditional Probabilities: Nonparametric.. Bayes Decision Rule.. Likelihood Ratio Approach.. 10.3 Evaluating the Classifier.. Independent Test Sample.. Cross-Validation.. Receiver Operating Characteristic (ROC Curve.. 10.4 Classification Trees.. Growing the Tree.. Pruning the Tree.. Choosing the Best Tree.. Other Tree Methods.. 10.5 Combining Classifiers.. Bagging.. Boosting.. Arcing Classifiers.. Random Forests.. 10.6 MATLAB® Code.. 10.7 Further Reading.. Exercises.. Chapter 11.. Unsupervised Learning.. 11.1 Introduction.. 11.2 Measures of Distance.. 11.3 Hierarchical Clustering.. 11.4 K-Means Clustering.. 11.5 Model-Based Clustering.. Finite Mixture Models and the EM Algorithm Model-Based Agglomerative Clustering.. Bayesian Information Criterion.. Model-Based Clustering Procedure 11.6 Assessing Cluster Results.. Mojena - Upper Tail Rule.. Silhouette Statistic.. Other Methods for Evaluating Clusters.. 11.7 MATLAB® Code.. 11.8 Further Reading.. Exercises.. Chapter 12.. Parametric Models.. 12.1 Introduction.. 12.2 Spline Regression Models.. 12.3 Logistic Regression.. Creating the Model.. Interpreting the Model Parameters.. 12.4 Generalized Linear Models.. Exponential Family Form.. Generalized Linear Model.. Model Checking.. 12.5 MATLAB® Code.. 12.6 Further Reading.. Exercises.. Chapter 13.. Nonparametric Models.. 13.1 Introduction.. 13.2 Some Smoothing Methods.. Bin Smoothing.. Running Mean.. Running Line.. Local Polynomial Regression - Loess.. Robust Loess.. 13.3 Kernel Methods.. Nadaraya-Watson Estimator

Local Linear Kernel Estimator.. 13.4 Smoothing Splines.. Natural Cubic Splines.. Reinsch Method for Finding Smoothing Splines.. Values for a Cubic Smoothing Spline.. Weighted Smoothing Spline.. 13.5 Nonparametric Regression - Other Details.. Choosing the Smoothing Parameter.. Estimation of the Residual Variance.. Variability of Smooths.. 13.6 Regression Trees.. Growing a Regression Tree.. Pruning a Regression Tree.. Selecting a Tree.. 13.7 Additive Models.. 13.8 MATLAB® Code.. 13.9 Further Reading.. Exercises.. Chapter 14.. Markov Chain Monte Carlo Methods.. 14.1 Introduction.. 14.2 Background.. Bayesian Inference.. Monte Carlo Integration.. Markov Chains.. Analyzing the Output.. 14.3 Metropolis-Hastings Algorithms.. Metropolis-Hastings Sampler.. Metropolis Sampler.. Independence Sampler.. Autoregressive Generating Density.. 14.4 The Gibbs Sampler.. 14.5 Convergence Monitoring.. Gelman and Rubin Method.. Raftery and Lewis Method.. 14.6 MATLAB® Code.. 14.7 Further Reading.. Exercises.. Chapter 15.. Spatial Statistics.. 15.1 Introduction.. What Is Spatial Statistics?.. Types of Spatial Data.. Spatial Point Patterns.. Complete Spatial Randomness.. 15.2 Visualizing Spatial Point Processes.. 15.3 Exploring First-order and Second-order Properties.. Estimating the Intensity.. Estimating the Spatial Dependence.. 15.4 Modeling Spatial Point Processes.. Nearest Neighbor Distances.. K-Function.. 15.5 Simulating Spatial Point Processes.. Homogeneous Poisson Process.. Binomial Process.. Poisson Cluster Process.. Inhibition Process.. Strauss Process.. 15.6 MATLAB® Code.. 15.7 Further Reading.. Exercises.. Appendix A.. Introduction to MATLAB®.. A.1 What Is MATLAB®?.. A.2 Getting Help in MATLAB®.. A.3 File and Workspace Management.. A... Punctuation in MATLAB®.. A.5 Arithmetic Operators.. A.6 Data Constructs in MATLAB®.. Basic Data Constructs.. Building Arrays.. Cell Arrays.. Script Files and Functions.

As with the bestselling first edition, Computational Statistics Handbook with MATLAB®, Second Edition covers some of the most commonly used contemporary techniques in computational statistics. With a strong, practical focus on implementing the methods, the authors include algorithmic descriptions of the procedures as well as examples that illustrate the use of the algorithms in data analysis. Updated for MATLAB® R2007a and the Statistics Toolbox, Version 6.0, this edition incorporates many additional computational statistics topics. New to the Second Edition • New functions for multivariate normal and multivariate t distributions • Updated information on the new MATLAB functionality for univariate and bivariate histograms, glyphs, and parallel coordinate plots • New content on independent component analysis, nonlinear dimensionality reduction, and multidimensional scaling • New topics on linear classifiers, quadratic classifiers, and voting methods, such as bagging, boosting, and random forests • More methods for unsupervised learning, including model-based clustering and techniques for assessing the results of clustering • A new chapter on parametric models that covers spline regression models, logistic regression, and generalized linear models • Expanded information on smoothers, such as bin smoothing, running mean and line smoothers, and smoothing splines With numerous problems and suggestions for further reading, this accessible text facilitates an understanding of computational statistics concepts and how they are employed in data analysis. Inglés